1. Field of the Invention
The present invention relates a universal data-mining platform capable of analyzing mass spectrometry (MS) serum proteomic profiles and/or gene array data to produce biologically meaningful classification; i.e., group together biologically related specimens. This platform utilizes the principles of phylogenetics, such as parsimony, to analyze the genetic, physiological, and developmental processes where deviation from the normal conditions of the population need to be assessed, profiled, or defined as well as assessing the normal physiological pathways. This may be used, for example, to reveal susceptibility to cancer development, diagnosis and typing of cancer, identifying stages of cancer, as well as post-treatment evaluation. Furthermore, the uniquely derived characters that it identifies are potential biomarkers for cancers and their subclasses.
2. Description of the Prior Art
Classifying specimens on the basis of their overall similarity (e.g., phenetic approaches such as clustering) is problematic. Comparability of proteomic analyses performed in diverse locations is unattainable due to the lack of broadly acceptable universal methods of analysis. Further, the use of mass spectrometry (MS) of serum proteins to produce clinically useful profiles has proven to be challenging, and has generated some controversy. Although several methods have been published thus far, they all have either had cancer type-specific sorting algorithms that produced below 95% specificity and did not apply well across other cancer types, did not utilize all potentially useful variability within the data, or were not widely tested. Furthermore, their relative success has been limited to diagnoses without any predictive conclusions. Since cancer is an evolutionary condition produced by a set of mutations, the present invention applies analysis that includes evolutionary sound methods of analysis.